Search

The easiest way for the majority of users to install pandas is to install it
as part of the Anaconda distribution, a
cross platform distribution for data analysis and scientific computing.
This is the recommended installation method for most users.

Instructions for installing from source,
PyPI, various Linux distributions, or a
development version are also provided.

Installing pandas and the rest of the NumPy and
SciPy stack can be a little
difficult for inexperienced users.

The simplest way to install not only pandas, but Python and the most popular
packages that make up the SciPy stack
(IPython, NumPy,
Matplotlib, ...) is with
Anaconda, a cross-platform
(Linux, Mac OS X, Windows) Python distribution for data analytics and
scientific computing.

After running a simple installer, the user will have access to pandas and the
rest of the SciPy stack without needing to install
anything else, and without needing to wait for any software to be compiled.

An additional advantage of installing with Anaconda is that you don’t require
admin rights to install it, it will install in the user’s home directory, and
this also makes it trivial to delete Anaconda at a later date (just delete
that folder).

The previous section outlined how to get pandas installed as part of the
Anaconda distribution.
However this approach means you will install well over one hundred packages
and involves downloading the installer which is a few hundred megabytes in size.

If you want to have more control on which packages, or have a limited internet
bandwidth, then installing pandas with
Miniconda may be a better solution.

Conda is the package manager that the
Anaconda distribution is built upon.
It is a package manager that is both cross-platform and language agnostic
(it can play a similar role to a pip and virtualenv combination).

Miniconda allows you to create a
minimal self contained Python installation, and then use the
Conda command to install additional packages.

The next step is to create a new conda environment (these are analogous to a
virtualenv but they also allow you to specify precisely which Python version
to install also). Run the following commands from a terminal window:

conda create -n name_of_my_env python

This will create a minimal environment with only Python installed in it.
To put your self inside this environment run:

source activate name_of_my_env

On Windows the command is:

activate name_of_my_env

The final step required is to install pandas. This can be done with the
following command:

pandas is equipped with an exhaustive set of unit tests covering about 97% of
the codebase as of this writing. To run it on your machine to verify that
everything is working (and you have all of the dependencies, soft and hard,
installed), make sure you have nose and run: